Zero-shot Learning (ZSL): Traditional ZSL methods [1]}, [2]}, [3]}, [4]}, [5]} learn to project the visual features onto a semantic embedding space via direct mapping or through a compatibility function. However, such direct mappings are likely to suffer from issues of seen class bias and hubness [6]}, [7]}. In contrast, the work of [8]} leverages joint multi-modal learning of visual and textual feature embeddings for the task of ZSL. Recently, generative approaches tackle the problem of seen class bias by generating unseen visual features from respective class embeddings[9]}, [10]}, [11]}, [12]}, [13]}, [14]}, [15]}, [16]}. However, all the aforementioned methods address only ZSL, where the domain remains unchanged during training and testing.
